{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Sebastian Raschka](http://www.sebastianraschka.com)\n",
    "\n",
    "[back](https://github.com/rasbt/matplotlib-gallery) to the `matplotlib-gallery` at [https://github.com/rasbt/matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 11/08/2014 \n",
      "\n",
      "CPython 3.4.1\n",
      "IPython 2.1.0\n",
      "\n",
      "matplotlib 1.3.1\n",
      "numpy 1.8.1\n"
     ]
    }
   ],
   "source": [
    "%watermark -u -v -d -p matplotlib,numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1.5em\">[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Formatting I: subplots, markers, colors, axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Subplots](#Subplots)\n",
    "\n",
    "    - [m x n subplots](#m-x-n-subplots)\n",
    "    \n",
    "    - [Labeling a subplot grid like a matrix](#Labeling-a-subplot-grid-like-a-matrix)\n",
    "\n",
    "    - [Shared X- and Y-axes](#Shared-X--and-Y-axes)\n",
    "    \n",
    "    - [Setting title and labels](#Setting-title-and-labels)\n",
    "    \n",
    "    - [Hiding redundant subplots](#Hiding-redundant-subplots)\n",
    "    \n",
    "- [Defining Colors](#Defining-colors)\n",
    "\n",
    "    - [3 ways to define colors](#3-ways-to-define-colors)\n",
    "    \n",
    "    - [matplotlib color names](#matplotlib-color-names)\n",
    "\n",
    "    - [Colormaps](#Colormaps)\n",
    "    \n",
    "    - [Gray-levels](#Gray-levels)\n",
    "    \n",
    "    - [Edgecolors for scatter plots](#Edgecolors-for-scatter-plots)\n",
    "    \n",
    "    - [Color gradients](#Color-gradients)\n",
    "\n",
    "- [Marker styles](#Marker-styles)\n",
    "\n",
    "- [Line styles](#Line-styles)\n",
    "\n",
    "- [Fancy and transparent legends](#Fancy-and-transparent-legends)\n",
    "\n",
    "- [Hiding axes](#Hiding-axes)\n",
    "\n",
    "    - [Hiding axis ticks and labels](#Hiding-axis-ticks-and-labels)\n",
    "    \n",
    "    - [Removing frame and ticks](#Removing-frame-and-ticks)\n",
    "    \n",
    "- [Aesthetic axis layout](#Aesthetic-axis-layout)\n",
    "    \n",
    "- [Custom tick labels](#Custom-tick-labels)\n",
    "\n",
    "    - [Text and rotation](#Text-and-rotation)\n",
    "    \n",
    "    - [Adding a constant value to axis labels](#Adding-a-constant-value-to-axis-labels)\n",
    "    \n",
    "- [Applying customization and settings globally](#Applying-customization-and-settings-globally)\n",
    "\n",
    "    - [Settings for the active session only](#Settings-for-the-active-session-only)\n",
    "\n",
    "    - [Modifying the matplotlibrc file](#Modifying-the-matplotlibrc-file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "vgtIRmKGcn0AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1063b4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "fig, ax = plt.subplots(2)\n",
    "\n",
    "for sp in ax:\n",
    "    sp.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### m x n subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "QmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106544a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "for row in ax:\n",
    "    for col in row:\n",
    "        col.plot(x, y)\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "QmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104847be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(x, y)\n",
    "    \n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(x, y)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labeling a subplot grid like a matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "yAAYfCIiIgNg8ImIiAyAwSciIjIABp+IiMgA/i/7/gBjk9DNlAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1089c79e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, axes = plt.subplots(nrows=3, ncols=3, \n",
    "                         sharex=True, sharey=True,\n",
    "                         figsize=(8,8)\n",
    "                         )\n",
    "x = range(5)\n",
    "y = range(5)\n",
    "\n",
    "for row in axes:\n",
    "    for col in row:\n",
    "        col.plot(x, y)\n",
    "\n",
    "for ax, col in zip(axes[0,:], ['1', '2', '3']):\n",
    "    ax.set_title(col, size=20)\n",
    "    \n",
    "for ax, row in zip(axes[:,0], ['A', 'B', 'C']):\n",
    "    ax.set_ylabel(row, size=20, rotation=0, labelpad=15)\n",
    "  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shared X- and Y-axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1057b8ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2,ncols=2, sharex=True, sharey=True)\n",
    "\n",
    "for row in ax:\n",
    "    for col in row:\n",
    "        col.plot(x, y)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting title and labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105abc358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "for row in ax:\n",
    "    for col in row:\n",
    "        col.plot(x, y)\n",
    "        col.set_title('title')  \n",
    "        col.set_xlabel('x-axis')\n",
    "        col.set_ylabel('x-axis')\n",
    "                \n",
    "fig.tight_layout()\n",
    "            \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hiding redundant subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes we create more subplots for a rectangular layout (here: 3x3) than we actually need. Here is how we hide those redundant subplots. Let's assume that we only want to show the first 7 subplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105602ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=3,ncols=3)\n",
    "\n",
    "for cnt, ax in enumerate(axes.ravel()):\n",
    "    if cnt < 7:    \n",
    "        ax.plot(x, y)\n",
    "    else:\n",
    "        ax.axis('off') # hide subplot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 ways to define colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib supports 3 different ways to encode colors, e.g, if we want to use the color blue, we can define colors as\n",
    "\n",
    "- RGB color values (range 0.0 to 1.0) -> `(0.0, 0.0, 1.0)`\n",
    "- matplotlib supported names -> `'blue'` or `'b'`\n",
    "- HTML hex values -> `'#0000FF'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1052cc0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "samples = range(1,4)\n",
    "\n",
    "for i, col in zip(samples, [(0.0, 0.0, 1.0), 'blue', '#0000FF']):\n",
    "    plt.plot([0, 10], [0, i], lw=3, color=col) \n",
    "\n",
    "plt.legend(['RGB values: (0.0, 0.0, 1.0)', \n",
    "            \"matplotlib names: 'blue'\", \n",
    "            \"HTML hex values: '#0000FF'\"],\n",
    "           loc='upper left')    \n",
    "plt.title('3 alternatives to define the color blue')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib color names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The color names that are supported by matplotlib are\n",
    "\n",
    "b: blue  \n",
    "g: green  \n",
    "r: red  \n",
    "c: cyan  \n",
    "m: magenta  \n",
    "y: yellow  \n",
    "k: black  \n",
    "w: white  \n",
    "    \n",
    "where the first letter represents the shortcut version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "URRFURRFURRFURRFURRFURRFURRFUZSA8P8BSWPRq5iuwn0AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105aaf7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cols = ['blue', 'green', 'red', 'cyan',  'magenta', 'yellow', 'black', 'white']\n",
    "\n",
    "samples = range(1, len(cols)+1)\n",
    "\n",
    "for i, col in zip(samples, cols):\n",
    "    plt.plot([0, 10], [0, i], label=col, lw=3, color=col) \n",
    "\n",
    "plt.legend(loc='upper left')    \n",
    "plt.title('matplotlib color names')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colormaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More color maps are available at [http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps](http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1053d2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(1,2, figsize=(14, 7))\n",
    "samples = range(1,16)\n",
    "\n",
    "# Default Color Cycle\n",
    "\n",
    "for i in samples:\n",
    "    ax0.plot([0, 10], [0, i], label=i, lw=3) \n",
    "\n",
    "# Colormap    \n",
    "    \n",
    "colormap = plt.cm.Paired\n",
    "plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, len(samples))])\n",
    "\n",
    "for i in samples:\n",
    "    ax1.plot([0, 10], [0, i], label=i, lw=3) \n",
    "    \n",
    "# Annotation    \n",
    "    \n",
    "ax0.set_title('Default color cycle')\n",
    "ax1.set_title('plt.cm.Paired colormap')\n",
    "ax0.legend(loc='upper left')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gray-levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "CIIgCIIgCIIgCIIgCIIgCGIS/P8RhFSUYntpEwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105433ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "samples = np.arange(0, 1.1, 0.1)\n",
    "\n",
    "for i in samples:\n",
    "    plt.plot([0, 10], [0, i], label='gray-level %s'%i, lw=3, \n",
    "             color=str(i)) # ! gray level has to be parsed as string\n",
    "\n",
    "plt.legend(loc='upper left')    \n",
    "plt.title('gray-levels')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Edgecolors for scatter plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AWNuwGgSGwsSAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106a28208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10,10))\n",
    "\n",
    "samples = np.random.randn(30,2)\n",
    "\n",
    "ax[0][0].scatter(samples[:,0], samples[:,1], \n",
    "            color='red',\n",
    "            label='color=\"red\"')\n",
    "    \n",
    "ax[1][0].scatter(samples[:,0], samples[:,1],\n",
    "            c='red',\n",
    "            label='c=\"red\"')\n",
    "\n",
    "ax[0][1].scatter(samples[:,0], samples[:,1], \n",
    "            edgecolor='white', \n",
    "            c='red',\n",
    "            label='c=\"red\", edgecolor=\"white\"')\n",
    "\n",
    "ax[1][1].scatter(samples[:,0], samples[:,1], \n",
    "            edgecolor='0', \n",
    "            c='1',\n",
    "            label='color=\"1.0\", edgecolor=\"0\"')\n",
    "\n",
    "for row in ax:\n",
    "    for col in row:\n",
    "        col.legend(loc='upper left') \n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Color gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "kgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1055b70b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as col\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "# input data\n",
    "mean_values = np.random.randint(1, 101, 100)\n",
    "x_pos = range(len(mean_values))\n",
    "\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "\n",
    "# create colormap\n",
    "cmap = cm.ScalarMappable(col.Normalize(min(mean_values), \n",
    "                                       max(mean_values), \n",
    "                                       cm.hot))\n",
    "\n",
    "# plot bars\n",
    "plt.subplot(131)\n",
    "plt.bar(x_pos, mean_values, align='center', alpha=0.5, \n",
    "        color=cmap.to_rgba(mean_values))\n",
    "plt.ylim(0, max(mean_values) * 1.1)\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.bar(x_pos, np.sort(mean_values), align='center', alpha=0.5, \n",
    "        color=cmap.to_rgba(mean_values))\n",
    "plt.ylim(0, max(mean_values) * 1.1)\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.bar(x_pos, np.sort(mean_values), align='center', alpha=0.5, \n",
    "        color=cmap.to_rgba(np.sort(mean_values)))\n",
    "plt.ylim(0, max(mean_values) * 1.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Marker styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "QooQHuGwAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105466da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "markers = [\n",
    "\n",
    "'.', # point\n",
    "',', # pixel\n",
    "'o', # circle\n",
    "'v', # triangle down\n",
    "'^', # triangle up\n",
    "'<', # triangle_left\n",
    "'>', # triangle_right\n",
    "'1', # tri_down\n",
    "'2', # tri_up\n",
    "'3', # tri_left\n",
    "'4', # tri_right\n",
    "'8', # octagon\n",
    "'s', # square\n",
    "'p', # pentagon\n",
    "'*', # star\n",
    "'h', # hexagon1\n",
    "'H', # hexagon2\n",
    "'+', # plus\n",
    "'x', # x\n",
    "'D', # diamond\n",
    "'d', # thin_diamond\n",
    "'|', # vline\n",
    "\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(13, 10))\n",
    "samples = range(len(markers))\n",
    "\n",
    "\n",
    "for i in samples:\n",
    "    plt.plot([i-1, i, i+1], [i, i, i], label=markers[i], marker=markers[i], markersize=10) \n",
    "\n",
    "\n",
    "# Annotation    \n",
    "    \n",
    "plt.title('Matplotlib Marker styles', fontsize=20)\n",
    "plt.ylim([-1, len(markers)+1])\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "vgAAWEb4AgBg2f8Dx6yNvuDNup8AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106a1d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "linestyles = ['-.', '--', 'None', '-', ':']\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "samples = range(len(linestyles))\n",
    "\n",
    "\n",
    "for i in samples:\n",
    "    plt.plot([i-1, i, i+1], [i, i, i], \n",
    "             label='\"%s\"' %linestyles[i], \n",
    "             linestyle=linestyles[i],\n",
    "             lw=4\n",
    "             ) \n",
    "\n",
    "# Annotation    \n",
    "    \n",
    "plt.title('Matplotlib line styles', fontsize=20)\n",
    "plt.ylim([-1, len(linestyles)+1])\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fancy and transparent legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "kJCQkJCQkJCQkJCQkJCQkJDg/wF0JneHfhwp/QAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1056915c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X1 = np.random.randn(100,2)\n",
    "X2 = np.random.randn(100,2)\n",
    "X3 = np.random.randn(100,2)\n",
    "\n",
    "R1 = (X1**2).sum(axis=1)\n",
    "R2 = (X2**2).sum(axis=1)\n",
    "R3 = (X3**2).sum(axis=1)\n",
    "\n",
    "plt.scatter(X1[:,0], X1[:,1],\n",
    "            c='blue',\n",
    "            marker='o',\n",
    "            s=32. * R1,\n",
    "            edgecolor='black',\n",
    "            label='Dataset X1',\n",
    "            alpha=0.7)\n",
    "plt.scatter(X2[:,0], X2[:,1],\n",
    "            c='gray',\n",
    "            marker='s',\n",
    "            s=32. * R2,\n",
    "            edgecolor='black',\n",
    "            label='Dataset X2',\n",
    "            alpha=0.7)\n",
    "\n",
    "plt.scatter(X2[:,0], X3[:,1],\n",
    "            c='green',\n",
    "            marker='^',\n",
    "            s=32. * R3,\n",
    "            edgecolor='black',\n",
    "            label='Dataset X3',\n",
    "            alpha=0.7)\n",
    "\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n",
    "\n",
    "leg = plt.legend(loc='upper left', fancybox=True)\n",
    "leg.get_frame().set_alpha(0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hiding axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hiding axis ticks and labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "nFRduuoWyE2VpatugVxVV7qr1fZFMHUL5Kia0t3V7c2NugXyVUXprlbbs9tfv9QtkLeiSzet26sr\n",
       "dQvkr9jSVbdAiYorXXULlKyo0nUzAShdEaXrZgJQi+xL19ktUJNsS9fZLVCjLEtX3QK1yqp01S1Q\n",
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       "AAAAAADgvX8AWUf4GhkpteIAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1053a2208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "fig = plt.gca()\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "fig.axes.get_xaxis().set_visible(False)\n",
    "fig.axes.get_yaxis().set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing frame and ticks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "ACcBDwO/m26cOS6nGkdxLAfe3r9/MvAfwIfTjXPEw8B5/ft/RYHLAI/Q10j/QNyM9xO2FE4ifB/e\n",
       "QfgtKLV39m/PIjwm9Y4Y/0gVT2K5h7DX9AvCHvmatOMA4eiIw4QfYjOHIF2VdCI4n7B/uY9wCNnN\n",
       "aceZ43KqcRTHOYTP0T7CN3pVvs7fTyjoxwlVWJWjOE4G/pejP9Sq4M85epjdHYTfZlN7mDDTPuCK\n",
       "xLNIkiRJkiRJkiRJkiRJkiRJkiSp6f4fOVgD8wraVAcAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10558e4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "fig = plt.gca()\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "# removing frame\n",
    "fig.spines[\"top\"].set_visible(False)  \n",
    "fig.spines[\"bottom\"].set_visible(False)  \n",
    "fig.spines[\"right\"].set_visible(False)  \n",
    "fig.spines[\"left\"].set_visible(False) \n",
    "\n",
    "# removing ticks\n",
    "plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "                labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aesthetic axis layout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "lLy8QgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1063bbf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X = np.random.normal(loc=0.0, scale=1.0, size=300)\n",
    "width = 0.5\n",
    "bins = np.arange(math.floor(X.min())-width, \n",
    "                 math.ceil(X.max())+width, \n",
    "                 width) # fixed bin size\n",
    "\n",
    "ax = plt.subplot(111)  \n",
    "\n",
    "# remove axis at the top and to the right\n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False) \n",
    "\n",
    "# hide axis ticks\n",
    "plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "                labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "\n",
    "plt.hist(X,  alpha=0.5, bins=bins)\n",
    "plt.grid()\n",
    "\n",
    "plt.xlabel('x label')\n",
    "plt.ylabel('y label')\n",
    "plt.title('title')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom tick labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text and rotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "NkmSJEmSJEmSJEmSJEmSJEmSpEH4fzPPvvDXhVcnAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105aaf358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "labels = ['super long axis label' for i in range(10)]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "# set custom tick labels\n",
    "ax.set_xticklabels(labels, rotation=45, horizontalalignment='right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a constant value to axis labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1065e4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "CONST = 10\n",
    "\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "labels = [i+CONST for i in x]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.xlabel('x-value + 10')\n",
    "\n",
    "# set custom tick labels\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying customization and settings globally"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everyone has a different perception of \"style\", and usually, we would make some little adjustments to matplotlib's default visuals here and there. After customization, it would be tedious to repeat the same code over and over again every time we produce a new plot.  \n",
    "However we have multiple options to apply the changes globally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings for the active session only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we are only interested in the settings for the current session. In this case, one way to customize matplotlibs defaults would be the 'rcParams' attribute (in the next session, you will see a handy reference for all the different matplotlib settings). E.g., if we want to make the font size of our titles larger for all plots that follow in the active session, we could type the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['axes.titlesize'] = '20'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "jFDQtxKqsCxHcewJ/C+Tf9TK4G+ZPMzuC4T/zRbtWsJMW4DjC55FkiRJkiRJkiRJkiRJkiRJkiRJ\n",
       "g+7/AX6sb+89aIHtAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1053aa7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.title('larger title')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we want to revert it back to the default settings, we can use the command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mpl.rcdefaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we have to re-execute the `matplotlib inline` magic function afterwards:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "tEEylwX6XibuLz2TUNGa3nzgS8B/Al8teJbJtgH/DRxR8BwvBV5H2PO9DHgVcGmhEwX3xe8fAr5C\n",
       "2N4r0s/it+/FH19OWKjL4nhgI+Hfq2hHADcCvyRsBX2Z8HVWtIsIsx1NqPo7sr6CMv8RyzLKdSfh\n",
       "EGGhuaDoQRL2JTwSAGB34Drgj4obZ4qjKcejOBYCe8aH9wBuAF5T3DhjrgMOjA83SPE0wH30eYq/\n",
       "I67jhYQthd0J34eXEH4LKtpT4vcHEO6T2iuPKynjH7FcRthr+h1hj3xFseMA4dEROwg/xDoPQTpu\n",
       "1nPk7xDC/uVmwkPIzip2nCmOphyP4ngW4d9oM+EbvSxf5y8kFPTNhCosy6M49gB+wfgPtTJ4H+MP\n",
       "s7uE8Nts0a4jzLQZOKbgWSRJkiRJkiRJkiRJkiRJkiRJkjTo/h8kGTOWzxf9rwAAAABJRU5ErkJg\n",
       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1054b0630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.title('default title size')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modifying the matplotlibrc file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assume that we decided to always prefer a particular setting over matplotlib's default (e.g., a larger font size for the title like in the previous section), we can make a change to the `matplotlibrc` file: This way we'd avoid to change the setting every time we start a new session or produce a new plot.\n",
    "\n",
    "The `matplotlibrc` file can reside in different places depending on your sytem. An convenient way to find out the location is to use the `matplotlib_fname()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/matplotlib/mpl-data/matplotlibrc'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.matplotlib_fname()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we open this file in an editor, we will see an overview of all the different matplotlib default settings and their default values. We can use this list either as a reference to apply changes dynamically (see the previous section), or we can un-comment this line here and change its default value. \n",
    "E.g., we want to change the title size again, we could change the following line:\n",
    "\n",
    "`axes.titlesize      : 20`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing to keep in mind is that this file becomes overwritten if we install a new version of matplotlib, and it is always recommended to keep backups as you change the settings (the file with its original default settings can be found [here](http://matplotlib.org/users/customizing.html#a-sample-matplotlibrc-file)).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, we even want to keep and use multiple `matplotlibrc` files, e.g., if we are writing articles for different journals and every journal has its own requirement for figure formats.\n",
    "In this case, we can load our own `rc` files from a different local directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import rc_file\n",
    "rc_file('/path/to/matplotlibrc_journalX')\n",
    "import matplotlib.pyplot as plt"
   ]
  }
 ],
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